We consider a bidirectional in-band full-duplex (FD) multiple-input multiple-output (MIMO) system subject to imperfect channel state information (CSI), hardware distortion, and limited analog cancellation capability as well as the self-interference (
SI) power requirement at the receiver analog domain so as to avoid the saturation of low noise amplifier (LNA). A novel minimum mean square error (MMSE)-based joint design of digital precoder and combiner for SI cancellation is offered, which combines the well-known gradient projection method and non-monotonicity considered in recent machine-learning literature in order to tackle the non-convexity of the optimization problem formulated in this article. Simulation results illustrate the effectiveness of the proposed SI cancellation algorithm.
We present a novel algorithm for the completion of low-rank matrices whose entries are limited to a finite discrete alphabet. The proposed method is based on the recently-emerged proximal gradient (PG) framework of optimization theory, which is appli
ed here to solve a regularized formulation of the completion problem that includes a term enforcing the discrete-alphabet membership of the matrix entries.
